sulie 1.0.4

A Python package for interacting with Sulie API

Sulie - Managed Time Series Forecasting with Mimosa Foundation Model

The Sulie SDK offers seamless integration with the Sulie platform for advanced time series forecasting powered by Mimosa—a transformer-based foundation model optimized specifically for time series data. Mimosa provides high accuracy for zero-shot forecasting and automatic fine-tuning, enabling tailored forecasts without extensive pre-training. From deployment to scaling, we handle the MLOps heavy lifting, so you can focus on making use of the forecasts.

Getting Started

To begin using the Sulie SDK, you’ll need an API key, which can be generated from the Sulie Dashboard:

1. Visit the Sulie Dashboard.
2. Sign in to your Sulie account.
3. Navigate to the API Keys section.
4. Generate a new API key and copy it to use within the SDK.

With your API key ready, you’re set to start forecasting.

Installation

To install the Sulie SDK, simply run:

pip install sulie

Quick Start Example

After installation, initialize the SDK using your API key to start forecasting with Mimosa:

from sulie import Sulie

# Initialize the Sulie client
client = Sulie(api_key="YOUR_API_KEY")

Features

1. Forecasting with Mimosa

Generate accurate time series forecasts using Mimosa’s zero-shot inference capabilities. This approach is ideal when you need fast, reliable predictions without training the model.

import pandas as pd

# Example time series data
df = pd.DataFrame({
    'timestamp': pd.date_range(start='2023-01-01', periods=1000, freq='H'),
    'demand': [ ... ],           # Demand data
    'location': ['Plant A', ...] # Data for different locations
})

# Forecast demand for each location over the next 24 hours
forecast = client.forecast(
    dataset=df,
    target='demand',
    group_by='location',
    date='timestamp',
    frequency='H',
    horizon=24,            # Predict 24 hours ahead
    num_samples=100        # Generate probabilistic forecasts
)
print(forecast)

Forecasting Parameters

Name	Description	Default
dataset	A Dataset or pd.DataFrame containing time series data.	Required
target	Column name for the forecast variable.	Required
group_by	Column name to group data by (e.g., different locations).	None
date	Timestamp column name.	None
frequency	Frequency of the time series (e.g., H for hourly).	None
horizon	Time steps to forecast ahead.	24
num_samples	Number of probabilistic forecast samples.	100

2. Fine-Tuning for Customized Forecasting

With automatic fine-tuning, you can optimize Mimosa for unique datasets and business cases. The fine-tuning process uses Weighted Quantile Loss (WQL) for evaluation, ensuring high accuracy.

# Fine-tune Mimosa on custom dataset
fine_tune_job = client.fine_tune(
    dataset=df,
    target="demand",
    description="Fine-tune for Plant A demand prediction"
)

# Check the fine-tuning job status
print(f"Job status: {fine_tune_job.status}")

Fine-Tuning Parameters

Name	Description	Default
dataset	A Dataset or pd.DataFrame with time series data.	Required
target	Target variable for optimization.	Required
description	Description of the fine-tuning job.	None

Once fine-tuning completes, the model is automatically deployed and available for forecasting.

3. Managing Datasets

Sulie’s Dataset API lets you manage and version your datasets, making them accessible for forecasting and fine-tuning across teams.

# Upload a dataset to Sulie
dataset = client.upload_dataset(
    name="product-sales-data-v1",
    df=df,
    mode="append"  # Choose 'append' or 'overwrite'
)

# List available datasets
datasets = client.list_datasets()
print(f"Available datasets: {datasets}")

Dataset Management Functions

• Upload: Store and version your data for easy access and updates.
• List: Retrieve a list of uploaded datasets.
• Update: Append or overwrite data for an existing dataset.

4. Forecasting with Custom Models

Fine-tuned models can be selected for new forecasts using list_custom_models or get_model.

# List custom and fine-tuned models
custom_models = client.list_custom_models()

# Select and forecast with a fine-tuned model
model_name = custom_models[0].name
custom_model = client.get_model(model_name)

# Forecast using the selected model
forecast_custom = custom_model.forecast(
    dataset=df,
    target='demand',
    group_by='location',
    date='timestamp',
    frequency='H',
    horizon=24,
    num_samples=50
)
print(forecast_custom)

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Additional Resources

• API Documentation: Full documentation with detailed usage.
• Forecasting Guide: Detailed parameters for Mimosa forecasting.
• Fine-Tuning Guide: Options and tuning customization.
• Support: Assistance and feedback on the SDK.